Factors affecting adherence to a gluten-free diet in children with celiac disease
Bibliographic record
Abstract
BACKGROUND: The treatment of celiac disease is a strict, life-long gluten-free (GF) diet. This diet is complex and can be challenging. Factors affecting adherence to the GF diet are important to identify for improving adherence. OBJECTIVE: To identify factors that inhibit or improve adherence to a GF diet in children with celiac disease. METHODS: Patients (<18 years of age) with biopsy-confirmed celiac disease followed by the gastroenterology service at a tertiary care paediatric institution were surveyed using a mailed questionnaire. Factors influencing adherence to a GF diet were scored from 1 to 10 based on how often they were problematic (1 = never, 10 = always). Parents of patients <13 years of age were instructed to complete the survey with their child. Adolescents ≥13 years of age were asked to complete the survey themselves. RESULTS: Of 253 subjects, 126 completed the survey; the median age was 12 years (range two to 18 years). Forty percent were adolescents. Overall, participants reported good adherence at home and school, but lower adherence at social events. Adolescents reported lower adherence compared with parents. Availability of GF foods and cost were the most significant barriers. Other factors identified to help with a GF diet included education for schools/restaurants and improved government support. CONCLUSIONS: Availability, cost and product labelling are major barriers to adherence to a GF diet. Better awareness, improved labelling and income support are needed to help patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".